Datasets:
File size: 2,858 Bytes
6fa2d2b 2ee89c8 6fa2d2b 2ee89c8 0cf90a6 027942e 0cf90a6 027942e 2ee89c8 0cf90a6 0c23a0e 2ee89c8 027942e 2ee89c8 027942e 2ee89c8 027942e 2ee89c8 027942e 2ee89c8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | ---
license: mit
task_categories:
- question-answering
- multiple-choice
language:
- en
tags:
- evaluation
- responsibleAI
- utility
size_categories:
- 10K<n<100K
---
<p align="center">
<strong>UtilityBench: Measuring Language Model Utility When Errors Have Different Costs</strong>
</p>
<p align="center">
<a href="https://arxiv.org/abs/2610.25924"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv" alt="arXiv"></a>
<a href="https://github.com/LQF39466/UtilityBench"><img src="https://img.shields.io/badge/GitHub-Code-181717?logo=github" alt="GitHub"></a>
</p>
## Introduction
As language models are increasingly deployed in real-world scenarios with important outcomes, their mistakes can have consequences that correctness alone does not capture. A minor error in a low-stakes question and a dangerous error in medical advice should not carry the same practical significance. We therefore propose a utility-based evaluation paradigm that explicitly incorporates the varying consequences of errors, encouraging more responsible model behavior. As an early trial of this paradigm, we construct UtilityBench with continuous, sample-specific consequence annotations and introduce UtilityAUC.
## Usage
Import to your project using huggingface dataset, or refer to the [github repository](https://github.com/LQF39466/UtilityBench) for an end-to-end demo.
## Dataset Fields
The dataset contains 15,000 records in a single `test` split.
| Field | Type | Description |
|---|---|---|
| `id` | int64 | Unique record identifier |
| `input` | string | Full prompt sent to the model |
| `answerability` | int8 | 1 = answerable; 0 = refusal required |
| `reference_answer` | string / null | Reference answer; null for refusal-required prompts |
| `source_id` | string | Source dataset and item identifier |
| `eta` | float64 | Relative error-consequence score; higher means greater consequences |
| `L` | float64 | Positive cost of an incorrect answer |
| `evaluator_id` | string | Evaluation rule or code-test identifier |
## Acknowledgement
UtilityBench builds on 18 source datasets. We thank their authors for making these resources publicly available. Record-level provenance is preserved in `source_id`.
## Citations
If you find this work useful, please consider citing our paper:
```
@misc{lei2026utilitybench,
author = {Qifeng Lei and Zhiyong Yang and Zitai Wang and Shilong Bao and Linchao Zhang and Qianqian Xu and Qingming Huang},
title = {{UtilityBench}: Measuring Language Model Utility When Errors Have Different Costs},
year = {2026},
eprint = {2610.25924},
archiveprefix = {arXiv},
}
```
## Contact
If you encounter any problems using the dataset or evaluating your models, please [open an issue](https://github.com/LQF39466/UtilityBench/issues) in the github repository. |